adapt geometric example script to new API and improve some expressions

doc/examples/applications/plot_geometric.py

@@ -25,24 +25,27 @@ affine, projective and polynomial.
 
 Geometric transformations can either be created using the explicit parameters
 (e.g. scale, shear, rotation and translation) or the transformation matrix:
+
+First we create a transformation using explicit parameters:
 """
 
-#: create using explicit parameters
-tform = tf.SimilarityTransform()
-scale = 1
-rotation = math.pi/2
-translation = (0, 1)
-tform.compose_implicit(scale, rotation, translation)
+tform = tf.SimilarityTransform(scale=1, rotation=math.pi / 2,
+                               translation=(0, 1))
 print tform._matrix
 
-#: create using transformation matrix
+"""
+Alternatively you can define a transformation by the transformation matrix
+itself:
+"""
+
 matrix = tform._matrix.copy()
 matrix[1, 2] = 2
 tform2 = tf.SimilarityTransform(matrix)
 
 """
-These transformation objects can be used to forward and reverse transform
-coordinates between the source and destination coordinate systems:
+These transformation objects can then be used to apply forward and inverse
+coordinate transformations between the source and destination coordinate
+systems:
 """
 
 coord = [1, 0]
@@ -57,26 +60,22 @@ Geometric transformations can also be used to warp images:
 """
 
 text = data.text()
-tform.compose_implicit(1, math.pi/4, (text.shape[0] / 2, -100))
 
-# uses tform.inverse, alternatively use tf.warp(text, tform.inverse)
+tform = tf.SimilarityTransform(scale=1, rotation=math.pi / 4,
+                               translation=(text.shape[0] / 2, -100))
+
 rotated = tf.warp(text, tform)
-back_rotated = tf.warp(rotated, tform)
+back_rotated = tf.warp(rotated, tform.inverse)
 
-plt.figure(figsize=(8, 3))
-plt.subplot(131)
-plt.imshow(text)
-plt.axis('off')
+fig, (ax1, ax2, ax3) = plt.subplots(ncols=3, figsize=(8, 3))
+fig.subplots_adjust(**margins)
 plt.gray()
-plt.subplot(132)
-plt.imshow(rotated)
-plt.axis('off')
-plt.gray()
-plt.subplot(133)
-plt.imshow(back_rotated)
-plt.axis('off')
-plt.gray()
-plt.subplots_adjust(**margins)
+ax1.imshow(text)
+ax1.axis('off')
+ax2.imshow(rotated)
+ax2.axis('off')
+ax3.imshow(back_rotated)
+ax3.axis('off')
 
 """
 .. image:: PLOT2RST.current_figure
@@ -88,7 +87,8 @@ In addition to the basic functionality mentioned above you can also estimate the
 parameters of a geometric transformation using the least-squares method.
 
 This can amongst other things be used for image registration or rectification,
-where you have a set of control points or homologous points in two images.
+where you have a set of control points or homologous/corresponding points in two
+images.
 
 Let's assume we want to recognize letters on a photograph which was not taken
 from the front but at a certain angle. In the simplest case of a plane paper
@@ -100,33 +100,30 @@ the image so that the distortion is removed and then apply a matching algorithm:
 text = data.text()
 
 src = np.array((
-    (155, 15),
-    (65, 40),
-    (260, 130),
-    (360, 95)
-))
-dst = np.array((
     (0, 0),
     (0, 50),
     (300, 50),
     (300, 0)
 ))
+dst = np.array((
+    (155, 15),
+    (65, 40),
+    (260, 130),
+    (360, 95)
+))
 
 tform3 = tf.ProjectiveTransform()
 tform3.estimate(src, dst)
 warped = tf.warp(text, tform3, output_shape=(50, 300))
 
-plt.figure(figsize=(8, 3))
-plt.subplot(211)
-plt.imshow(text)
-plt.plot(src[:, 0], src[:, 1], '.r')
-plt.axis('off')
+fig, (ax1, ax2) = plt.subplots(nrows=2, figsize=(8, 3))
+fig.subplots_adjust(**margins)
 plt.gray()
-plt.subplot(212)
-plt.imshow(warped)
-plt.axis('off')
-plt.gray()
-plt.subplots_adjust(**margins)
+ax1.imshow(text)
+ax1.plot(dst[:, 0], dst[:, 1], '.r')
+ax1.axis('off')
+ax2.imshow(warped)
+ax2.axis('off')
 
 """
 .. image:: PLOT2RST.current_figure